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%load_ext autoreload
%autoreload 2
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import os
MOMAPS_HOME = '/home/labs/hornsteinlab/Collaboration/MOmaps_Noam/MOmaps'
MOMAPS_DATA_HOME = '/home/labs/hornsteinlab/Collaboration/MOmaps'
LOGS_PATH = os.path.join(MOMAPS_HOME, 'outputs', 'preprocessing', 'spd','logs','no_downsample')
PLOT_PATH = os.path.join(MOMAPS_HOME, 'src', 'preprocessing', 'notebooks','figures','Neurons')
os.chdir(MOMAPS_HOME)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid", font_scale=1.5)
sns.color_palette("husl", 8)
plt.rcParams["image.cmap"] = "Set1"
from tqdm.notebook import tqdm
from src.common.lib.preprocessing_utils import rescale_intensity
from src.common.lib.images_qc import *
#sys.path.insert(1, "/home/labs/hornsteinlab/Collaboration/MOmaps_Sagy/MOmaps/src/common/lib")
import contextlib
import io
import matplotlib
import warnings
warnings.filterwarnings('ignore', category=pd.core.common.SettingWithCopyWarning)
from src.common.lib.qc_config_tmp import *
from src.common.lib.image_sampling_utils import *
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df = log_files_qc(LOGS_PATH)
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# choose batches
#batches = [f'batch{i}' for i in range (3,7)] + ['batch7_16bit','batch8_16bit','batch9_16bit']
batches=[f'batch{i}_16bit_no_downsample' for i in range(3,10)]
batches
Raw Files¶
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root_directory_raw = os.path.join(MOMAPS_DATA_HOME, 'input', 'images', 'raw', 'SpinningDisk')
batches_raw = [batch.replace("_16bit_no_downsample","") for batch in batches]
raws = run_validate_folder_structure(root_directory_raw, False, panels, markers,PLOT_PATH,marker_info,
cell_lines_to_cond, reps, cell_lines_for_disp, expected_dapi_raw,
batches=batches_raw)
Processed¶
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root_directory_proc = os.path.join(MOMAPS_DATA_HOME, 'input', 'images', 'processed', 'spd2',
'SpinningDisk')
procs = run_validate_folder_structure(root_directory_proc, True, panels, markers,PLOT_PATH,marker_info,
cell_lines_to_cond, reps, cell_lines_for_disp, expected_dapi_raw,
batches=batches)
Difference between Raw and Processed¶
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display_diff(batches, raws, procs, PLOT_PATH)
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#for batch in list(range(3,9)) + ['7_16bit','8_16bit','9_16bit']:
for batch in batches:
with contextlib.redirect_stdout(io.StringIO()):
var = sample_and_calc_variance(root_directory_proc, batch,
sample_size_per_markers=200, num_markers=26)
print(f'{batch} var: ',var)
Number of sites in each batch and cell line ¶
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df_no_per = df[df.batch!='Perturbations']
plot_sites_count(df_no_per, expected_raw, lines_order, custom_palette, split_to_reps=True)
Number of Cells in Site for each batch and cell line ¶
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df_no_empty_sites = df_no_per[df_no_per.n_valid_tiles !=0]
plot_cell_count(df_no_empty_sites, lines_order, custom_palette, whole_cells=True)
plot_cell_count(df_no_empty_sites, lines_order, custom_palette, whole_cells=False)
# can add norm=True to norm by max
number of valid tiles per image (site)¶
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plot_n_valid_tiles_count(df_no_per, custom_palette,reps, batch_min=3, batch_max=9)
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plot_p_valid_tiles_count(df_no_per, custom_palette,reps, batch_min=3, batch_max=9)
Heatmap QC per batch, panel and cell line(tiles that passed QC condition) ¶
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plot_hm(df_no_per, split_by='rep', rows='cell_line', columns='panel')
Assessing Staining Reproducibility and Outliers¶
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for batch in batches:
print(batch)
# if 'batch7' in batch:
# continue
#batch_num = batch.replace('batch',"")
run_calc_hist_new(batch,cell_lines_for_disp, markers, ncols=7, nrows=4,
hist_sample=10,sample_size_per_markers=200)
print("="*30)
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# save notebook as HTML ( the HTML will be saved in the same folder the original script is)
from IPython.display import display, Javascript
display(Javascript('IPython.notebook.save_checkpoint();'))
os.system('jupyter nbconvert --to html src/preprocessing/notebooks/no_ds/cell_count_stats_analysis_no_downsample.ipynb')
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